A compact dual wide-band multiple-input-multiple-output (MIMO) radiator is presented in this article. The MIMO radiator is developed on a 1.5 mm thick FR-4 substrate. The proposed structure is composed of U-shaped patches which are connected with small rectangular slit loaded circular rings and partial ground plane with chamfer edges. This arrangement is placed symmetrically at a far distance of 2.5 mm to achieve dual-operating bands with a high amount of isolation, radiation efficiencies and group delay between two radiators. The proposed compact dual-band radiator is resonating at 2.5/6.4 GHz frequencies and it provides isolation greater than 20 dB over the operating bands. The proposed design has a size of 0.45λ 0 × 0.291λ 0 × 0.03λ 0 mm and |S 11 | of about 41.25 dB and 14.58 dB. This design has a wide impedance matching in the range of frequencies from 2.38 GHz to 3.17 GHz & 5.14 GHz to 7.39 GHz with |S 21 | < 15 dB. The performance of diversity is calculated through diversity gain (DG) higher than 9.99, and Envelope Correlation Coefficient (ECC) which is less than 0.04 over the operating bands.
Extraction of water bodies from satellite imagery has been broadly explored in the current decade. So many techniques were involved in detecting of the surface water bodies from satellite data. To detect and extracting of surface water body changes in Nagarjuna Sagar Reservoir, Andhra Pradesh from the period 1989 to 2017, were calculated using Landsat-5 TM, and Landsat-8 OLI data. Unsupervised classification and spectral water indexing methods, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), and Modified Normalized Difference Water Index (MNDWI), were used to detect and extraction of the surface water body from satellite data. Instead of all index methods, the MNDWI was performed better results. The Reservoir water area was extracted using spectral water indexing methods (NDVI, NDWI, MNDWI, and NDMI) in 1989, 1997, 2007, and 2017. The shoreline shrunk in the twenty-eight-year duration of images. The Reservoir Nagarjuna Sagar lost nearly around one-fourth of its surface water area compared to 1989. However, the Reservoir has a critical position in recent years due to changes in surface water and getting higher mud and sand. Maximum water surface area of the Reservoir will lose if such decreasing tendency follows continuously.
Mel Frequency Cepstral Coefficient (MFCC) method is a feature extraction technique used for speech signals. In machine learning systems, the Random Subspace Method (RSM) known as attribute bagging or bagged featuring used to classify the complete feature sets. In this paper, an innovative method is proposed which is a combination of RSM and kNN algorithm known as Subspace-kNN (S-kNN) classifier. The classifier selects the specific features extracted from MFCC are angry, sad, fear, disgust, calm, happiness, surprise, and neutral speech emotions in Speech Emotion Recognition (SER) system. Furthermore, in the proposed method the performance metrics of accuracy, Positive Predictive Values (PPV) rate, training time are evaluated on male and female voice signals when compared with previous classifiers like SVM and bagged trees.
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