The nude mice injected with human gastric cancer cells (SGC-7901) in their peritoneums were chosen as the animal models of gastric cancer peritoneal dissemination in this research. The Raman spectra at 785nm excitatio...
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ISBN:
(纸本)9780819486844
The nude mice injected with human gastric cancer cells (SGC-7901) in their peritoneums were chosen as the animal models of gastric cancer peritoneal dissemination in this research. The Raman spectra at 785nm excitation of both these nude mice which were in different tumor planting periods and the normal counterpart were taken in vivo in the imitate laparotomy. 205 spectra were collected. The spectra of different tissue types were compared and classified by Support Vector Machine (svm) algorithm. Significant differences were showed between normal and malignant tissues. The gastric cancer nodules had lower Raman intensities at 870, 1330, 1450, and 1660cm(-1), but higher at 1007, 1050, 1093 and 1209cm(-1), compared with normal tissues. Additionally, the spectra of malignant tissues had two peaks around 1330 cm(-1) (1297cm(-1) and 1331cm(-1)), while the spectra of normal tissues had only one peak (1297cm(-1)). The differences were attributed to the intensities of the stretching bands of the nucleic acid, protein and water. These features could be used to diagnose gastric cancer. The Support Vector Machine (svm) algorithm was used to classify these spectra. For normal and malignant tissues, the sensitivity, specificity and accuracy were 95.73%, 70.73% and 90.73%, respectively, while for different tumor planting periods, they were 98.82%, 98.73% and 98.78%. The experimental results show that Raman spectra differ significantly between cancerous and normal gastric tissues, which provides the experimental basis for the diagnosis of gastric cancer by Raman spectroscopy technology. And svm algorithm can give the well generalized classification performance for the samples, which expands the application of mathematical algorithms in the classification.
In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past ...
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ISBN:
(纸本)9781424473021
In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past data for one source of time series images may be limited, so we need to develop multi-temporal and multi-source method for wetland ecosystem monitoring. To perform this type of analysis, the image spectral characteristics comparison between different aquatic macrophytes and different sensors should be studied firstly. We used TM images, Beijing-1 images and HJ-1 images for this analysis and based on the determination of aquatic plant functional types (PFTs). The objectives of this study were: (1) single-sensor single-date aquatic PFT analysis;(2) multi-source single-date diagnostic spectral characteristics analysis and comparison for different aquatic PFTs;(3) multi-source multi-temporal diagnostic spectral characteristics analysis for different aquatic PFTs. From this analysis we found that: (1) For the single-date TM data, the diagnostic spectral band and indexes are Band 2, 4, 5, NDVI, and MNDWI;the best temporal for discriminating different Nonpersistent Emergent Wetland PFTs are in low water level periods, and water infilling and subsiding periods for seasonal submerged and floating aquatic macrophyte. Multi-spectral Decision Tree classification method lead the more good results for most of PFTs;(2) the same type of aquatic PFTs have similar and comparable reflectance characteristics between multi-sensor optical data which could satisfy the time series analysis by compensating more available past images;(3) phenological curves and relative canopy moisture curves extracted from time series remote sensing images provide important information for distinguish different PFTs.
An automatic Uni- or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography ...
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ISBN:
(纸本)9781424441242
An automatic Uni- or Multi-modal Inteligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic algorithm on motion data is extracting features as "log-sum" measures of discrete wavelet components. Classification into the two groups "seizure" versus "non-seizure" is made based on the support vector machine (svm) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1 second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multi-modal approach, as compared with the uni-modal one.
In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past ...
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ISBN:
(纸本)9781424473014
In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community-and species-level discrimination. And, since the availability of past data for one source of time series images may be limited, so we need to develop multi-temporal and multi-source method for wetland ecosystem monitoring. To perform this type of analysis, the image spectral characteristics comparison between different aquatic macrophytes and different sensors should be studied firstly. We used TM images, Beijing-1 images and HJ-1 images for this analysis and based on the determination of aquatic plant functional types (PFTs). The objectives of this study were: ⑴ single-sensor single-date aquatic PFT analysis;⑵ multi-source single-date diagnostic spectral characteristics analysis and comparison for different aquatic PFTs;⑶ multi-source multitemporal diagnostic spectral characteristics analysis for different aquatic PFTs. From this analysis we found that: ⑴ For the single-date TM data, the diagnostic spectral band and indexes are Band 2, 4, 5, NDVI, and MNDWI;the best temporal for discriminating different Nonpersistent Emergent Wetland PFTs are in low water level periods, and water infilling and subsiding periods for seasonal submerged and floating aquatic macrophyte. Multispectral Decision Tree classification method lead the more good results for most of PFTs;⑵ the same type of aquatic PFTs have similar and comparable reflectance characteristics between multi-sensor optical data which could satisfy the time series analysis by compensating more available past images;⑶ phenological curves and relative canopy moisture curves extracted from time series remote sensing images provide important information for distinguish different PFTs.
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