Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Dual-Branch Feature Fusion Network for Salient Object Detection

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

Recordkeeping culture in Switzerland: the impact of language and communication on the success of recordkeeping initiatives

Purpose Records management has been heavily influenced by practice in English-speaking countries but is often seen as a foreign import in non-Anglophone countries. This study aims to investigate how using English terminology or translating records management terminology into French in a Francophone environment impacts on the success of recordkeeping strategies. Design/methodology/approach Semi-structured interviews were conducted with Francophone archivists and records managers in Switzerland to assess their communication strategies and the language used to communicate recordkeeping objectives. Findings The research findings indicate that in a Francophone environment, archivists and records managers who use French terminology are more successful in promoting recordkeeping objectives than those who use English terminology. Given that research was limited to one Swiss canton, more research is needed to test these findings in other Francophone cantons, provinces and countries. Originality/value This study is important for the success of recordkeeping initiatives in non-Anglophone countries. It highlights the need to take into account the local information culture and use terminology with which people are most familiar.

The Consensus of Different Fractional-Order Chaotic Multiagent Systems Using Adaptive Protocols

This paper is concerned with the adaptive consensus problem of incommensurate chaotic fractional order multiagent systems. Firstly, we introduce fractional-order derivative in the sense of Caputo and the classical stability theorem of linear fractional order systems; also, algebraic graph theory and sufficient conditions are presented to ensure the consensus for fractional multiagent systems. Furthermore, adaptive protocols of each agent using local information are designed and a detailed analysis of the leader-following consensus is presented. Finally, some numerical simulation examples are also given to show the effectiveness of the proposed results.

A Deep Learning Approach for NeoAG-Specific Prediction Considering Both HLA-Peptide Binding and Immunogenicity: Finding Neoantigens to Making T-Cell Products More Personal

[Background]: An emerging type of cancer treatment, known as cell immunotherapy, is gaining popularity over chemotherapy or other radia-tion therapy that causes mass destruction to our body. One favourable ap-proach in cell immunotherapy is the use of neoantigens as targets that help our body immune system identify the cancer cells from healthy cells. Neoan-tigens, which are non-autologous proteins with individual specificity, are generated by non-synonymous mutations in the tumor cell genome. Owing to its strong immunogenicity and lack of expression in normal tissues, it is now an important target for tumor immunotherapy. Neoantigens are some form of special protein fragments excreted as a by-product on the surface of cancer cells during the DNA mutation at the tumour. In cancer immunotherapies, certain neoantigens which exist only on cancer cells elicit our white blood cells (body's defender, anti-cancer T-cell) responses that fight the cancer cells while leaving healthy cells alone. Personalized cancer vaccines there-fore can be designed de novo for each individual patient, when the specific neoantigens are found to be relevant to his/her tumour. The vaccine which is usually coded in synthetic long peptides, RNA or DNA representing the neo-antigens trigger an immune response in the body to destroy the cancer cells (tumour). The specific neoantigens can be found by a complex process of biopsy and genome sequencing. Alternatively, modern technologies nowa-days tap on AI to predict the right neoantigen candidates using algorithms. However, determining the binding and non-binding of neoantigens on T-cell receptors (TCR) is a challenging computational task due to its very large search space. [Objective]: To enhance the efficiency and accuracy of traditional deep learning tools, for serving the same purpose of finding potential responsive-ness to immunotherapy through correctly predicted neoantigens. It is known that deep learning is possible to explore which novel neoantigens bind to T-cell receptors and which ones don't. The exploration may be technically ex-pensive and time-consuming since deep learning is an inherently computa-tional method. one can use putative neoantigen peptide sequences to guide personalized cancer vaccines design. [Methods]: These models all proceed through complex feature engineering, including feature extraction, dimension reduction and so on. In this study, we derived 4 features to facilitate prediction and classification of 4 HLA-peptide binding namely AAC and DC from the global sequence, and the LAAC and LDC from the local sequence information. Based on the patterns of sequence formation, a nested structure of bidirectional long-short term memory neural network called local information module is used to extract context-based features around every residue. Another bilstm network layer called global information module is introduced above local information module layer to integrate context-based features of all residues in the same HLA-peptide binding chain, thereby involving inter-residue relationships in the training process. introduced. [Results]: Finally, a more effective model is obtained by fusing the above two modules and 4 features matric, the method performs significantly better than previous prediction schemes, whose overall r-square increased to 0.0125 and 0.1064 on train and increased to 0.0782 and 0.2926 on test da-tasets. The RMSE for our proposed models trained decreased to approxi-mately 0.0745 and 1.1034, respectively, and decreased to 0.6712 and 1.6506 on test dataset. [Conclusion]: Our work has been actively refining a machine-learning model to improve neoantigen identification and predictions with the determinants for Neoantigen identification. The final experimental results show that our method is more effective than existing methods for predicting peptide types, which can help laboratory researchers to identify the type of novel HLA-peptide binding. Keywords: machine learning; Cancer Cell Immunology; HLA-peptide binding Neoantigen Prediction; HLA; Data Visualization; Novel Neoanti-gen and TCR Pairing Discovery; Vector representation

Local information pattern descriptor for corneal diseases diagnosis

<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>