Development of a document classification method by using geodesic distance to calculate similarity of documents

Currently, the Internet has given people the opportunity to access to human knowledge quickly and conveniently through various channels such as Web pages, social networks, digital libraries, portals... However, with the process of exchanging and updating information quickly, the volume of information stored (in the form of digital documents) is increasing rapidly. Therefore, we are facing challenges in representing, storing, sorting and classifying documents.In this paper, we present a new approach to text classification. This approach is based on semi-supervised machine learning and Support Vector Machine (SVM). The new point of the study is that instead of calculating the distance between the vectors by Euclidean distance, we use geodesic distance. To do this, the text must first be expressed as an n-dimensional vector. In the n-dimensional vector space, each vector is represented by one point; use geodesic distance to calculate the distance from a point to nearby points and connect into a graph. The classification is based on calculating the shortest path between vertices on the graph through a kernel function. We conducted experiments on articles taken from Reuters on 5 different topics. To evaluate the proposed method, we tested the SVM method with the traditional calculation based on Euclidean distance and the method we proposed based on geodesic distance. The experiment was performed on the same data set of 5 topics: Business, Markets, World, Politics, and Technology. The results showed that the correct classification rate is better than the traditional SVM method based on Euclidean distance (average of 3.2 %

Development of a document classification method by using geodesic distance to calculate similarity of documents

Currently, the Internet has given people the opportunity to access to human knowledge quickly and conveniently through various channels such as Web pages, social networks, digital libraries, portals... However, with the process of exchanging and updating information quickly, the volume of information stored (in the form of digital documents) is increasing rapidly. Therefore, we are facing challenges in representing, storing, sorting and classifying documents.In this paper, we present a new approach to text classification. This approach is based on semi-supervised machine learning and Support Vector Machine (SVM). The new point of the study is that instead of calculating the distance between the vectors by Euclidean distance, we use geodesic distance. To do this, the text must first be expressed as an n-dimensional vector. In the n-dimensional vector space, each vector is represented by one point; use geodesic distance to calculate the distance from a point to nearby points and connect into a graph. The classification is based on calculating the shortest path between vertices on the graph through a kernel function. We conducted experiments on articles taken from Reuters on 5 different topics. To evaluate the proposed method, we tested the SVM method with the traditional calculation based on Euclidean distance and the method we proposed based on geodesic distance. The experiment was performed on the same data set of 5 topics: Business, Markets, World, Politics, and Technology. The results showed that the correct classification rate is better than the traditional SVM method based on Euclidean distance (average of 3.2 %

BER of high-speed OFDM systems in the presence of offset mismatch of TI-ADCs

Time-interleaved analog-to-digital converters (TI-ADCs) are widely used for multi-Gigabit orthogonal frequency division multiplexing (OFDM) systems because of their attractive high sampling rate and high resolution. However, in practice, offset mismatch, one of the major mismatches of TI-ADCs, can occur between the parallel sub-ADCs. In this poster session, we theoretically analyze the BER performance of high-speed OFDM systems using TI-ADCs with offset mismatch. Gray-coded PAM or QAM signaling over an additive white Gaussian noise channel is considered. Our numerical results show that the obtained theoretical BER expressions are in excellent agreement with the simulated BER performance

BER analysis of high-speed OFDM systems in the presence of time-interleaved analog-to-digital converter's offset mismatch

Time-interleaved analog-to-digital converters (TI-ADCs) are widely used for multi-Gigabit orthogonal frequency division multiplexing (OFDM) systems because of their attractive high sampling rate and high resolution. However, mismatch between the parallel sub-ADCs can severely degrade the system performance. Several types of mismatch can be distinguished, one particular kind of mismatch is offset mismatch, which originates from the different DC offsets in the different sub-ADCs. Although some authors have studied the effect of offset mismatch on the bit error rate (BER) performance, exact close-form BER expressions in the presence of offset mismatch have not been derived yet. In this poster, we derive such BER expressions. Gray-coded PAM or QAM signaling over an additive white Gaussian noise channel is considered. Our numerical results show that the obtained theoretical BER expressions are in excellent agreement with the simulated BER performance. We also investigate simplified expressions for the error floor occurring at large SNR and large offset mismatch. Our finding shows that this error floor is essentially independent of the modulation order and the type of modulation

Size-dependent behaviour of functionally graded sandwich microbeams based on the modified couple stress theory

Abstract Static bending, buckling and free vibration behaviours of size-dependent functionally graded (FG) sandwich microbeams are examined in this paper based on the modified couple stress theory and Timoshenko beam theory. To avoid the use of a shear correction factor, equilibrium equations were used to compute the transverse shear force and shear stress. Two types of sandwich beams were considered: (1) homogeneous core and FG skins and (2) FG core and homogeneous skins. Numerical results were presented to illustrate the small scale effects on the behaviours of FG sandwich beams. The results reveals that the inclusion of the size effects results in an increase in the beam stiffness, and consequently, leads to a reduction of deflections and stresses and an increase in natural frequencies and critical buckling loads. Such effects are more pronounced when the beam depth was small, but they become negligible with the increase of the beam depth

A nonlocal sinusoidal plate model for micro/nanoscale plates

A nonlocal sinusoidal plate model for micro/nanoscale plates is developed based on Eringen’s nonlocal elasticity theory and sinusoidal shear deformation plate theory. The small scale effect is considered in the former theory while the transverse shear deformation effect is included in the latter theory. The proposed model accounts for sinusoidal variations of transverse shear strains through the thickness of the plate, and satisfies the stress-free boundary conditions on the plate surfaces, thus a shear correction factor is not required. Equations of motion and boundary conditions are derived from Hamilton’s principle. Analytical solutions for bending, buckling, and vibration of simply supported plates are presented, and the obtained results are compared with the existing solutions. The effects of small scale and shear deformation on the responses of the micro/nanoscale plates are investigated