Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support VECTOR Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency REPRESENTATION of raw data. Secondly, a four-element feature VECTOR is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency REPRESENTATION, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature VECTOR, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency REPRESENTATION of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

Text clustering and classification are two main tasks of text mining. Feature selection plays a key role in the quality of the clustering and classification results. Although word-based features such as Term Frequency-Inverse Document Frequency (TF-IDF) VECTORs have been widely used in different applications, their shortcomings in capturing semantic concepts of text have motivated researches to use semantic models for document VECTOR REPRESENTATIONs. The Latent Dirichlet Allocation (LDA) topic modeling and doc2vec neural document embedding are two well-known techniques for this purpose. In this work, we first studied the conceptual difference between the two models and showed that they had different behaviors and capture semantic features of texts from different perspectives. We then proposed a hybrid approach for document VECTOR REPRESENTATION to benefit from the advantages of both models. The experimental results on 20newsgroup showed the superiority of the proposed model compared to each one of the baselines on both text clustering and classification tasks. We achieved a 2. 6% improvement in F-measure for text clustering and a 2. 1% improvement in F-measure in text classification compared to the best baseline model.

We continue the study begun by the third author of C*-Segal algebra-valued function algebras with an emphasis on the order structure. Our main result is a characterization theorem for C*-Segal algebra-valued function algebras with an order unitization. As an intermediate step, we establish a function algebraic description of the multiplier module of arbitrary Segal algebra-valued function algebras. We also consider the Gelfand REPRESENTATION of these algebras in the commutative case.

Aims and Background: Nowadays, monoclonal antibodies (mAbs) became powerful therapeutic and diagnostic tools. Due to showing minimum immunogenic reaction and their properties, human mAbs are important. The purpose of this study was to construct an immune antibody library from a vaccinated donor against tetanus toxin.Materials and Methods: A whole blood sample was taken from the donor who was vaccinated against tetanus toxoid. PBMC were isolated by using ficol. After RNA extraction, all variation of VH and VL regions by RT-PCR reactions were amplified and linked as a ScFv antibody. The amplicons were inserted in T-VECTOR and transformed to E. coli DH5α strain, followed by an ELISA test. Plasmids were also extracted and sequenced.Results: cDNA quality was confirmed by using HPRT primers. To confirm PCR, insertion and transformation, gel electrophoresis and restriction enzymes digestion were applied. Positive clones were selected based on growth on LB agar which is the blue/white selection method. After plasmid extraction and DNA sequencing, the sequences were aligned using igBLAST at NCBI. The result was shown to have admissible similarity among antibody gene library nucleotide sequences and the antibody genes were deposited in this database. ELISA confirmed this data too.Conclusion: In this study, the immune human antibody library was constructed and confirmed using DNA sequencing and sequences alignment in NCBI database. ELISA test confirms antibody specifically. The next step is to screen the library to find an antibody specifically for tetanus toxin.