Recent Advances in Computer Science and Communications - Volume 16, Issue 4, 2023

Background: Spectrum is the backbone for wireless communications, including internet services. Nowadays, the business of industries providing wired communication is constant while the business of industries dealing with wireless communications is growing very fast. There is a large demand for radio spectrum for new wireless multimedia services. Although the present fixed spectrum allotment schemes do not cause any interference between users, but this fixed scheme of spectrum allocation does not allow accommodating the spectrum required for new wireless services. Cognitive radio (CR) relies on spectrum sensing to discover available frequency bands so that the spectrum can be used to its full potential, thus avoiding interference to the primary users (PU). Objective: The purpose of this work is to present an in-depth overview of traditional as well as advanced artificial intelligence and machine learning-based cooperative spectrum sensing (CSS) in cognitive radio networks. Methods: Using the principles of artificial intelligence (AI), systems are able to solve issues by mimicking the function of human brains. Moreover, since its inception, machine learning has demonstrated that it is capable of solving a wide range of computational issues. Recent advancements in artificial intelligence techniques and machine learning (ML) have made it an emergent technology in spectrum sensing. Results: The result shows that more than 80% of papers are on traditional spectrum sensing, while less than 20% deals with artificial intelligence and machine learning approaches. More than 75% of papers address the limitation of local spectrum sensing. The study presents the various methods implemented in spectrum sensing along with their merits and challenges. Conclusion: Spectrum sensing techniques are hampered by various issues, including fading, shadowing, and receiver unpredictability. Challenges, benefits, drawbacks, and scope of cooperative sensing are examined and summarized. With this survey article, academics may clearly know the numerous conventional artificial intelligence and machine learning methodologies used and can connect sharp audiences to contemporary research done in cognitive radio networks, which is now underway.

Background: In this research, a novel algorithm is formulated through the combination of gradient and adaptive thresholding. A set of 5 x 5 convolution kernels were generated to determine the gradients in the four main directions of the image. Objectives: The researcher converted the gaussian equation into a normalized kernel, which was convolved with the gradients to suppress the impact of noise. Methods: The edges derived were partitioned into a set of 5 x 5 matrices. A weighted variance was calculated for each local window in the image. The pixel that generated the minimum variance was used for the segmentation process in each local window. The researcher then trimmed multiple pixel width edges into singles by developing a set of 5 x 5 Structuring Elements (SE). These elements were placed over the image to remove boundary pixels. In order to produce colored edges, the algorithm was executed over all the channels and the results were concatenated to produce the skeletal colored edges. Results: From the evaluations conducted, the proposed algorithm exhibited better performance than most of the recent algorithms with respect to Human Perception Clarity and time complexity in both noisy and nonuniform illuminated images. Conclusion: The reason for this performance is that it is able to extract edges moving in the various directions of images. It also ensures that identified edges are single pixel width instead of multiple.

This research is conducted to study a fuzzy system with an improved rule base. The rule base is an important part of any fuzzy inference system designed. The rules of a fuzzy system depend on the number of features selected. Selecting an optimized number of features is called feature selection. All features (parameters) play an important role in the input to the system, but they have a different impact on the system performance. Some features do not even have a positive impact on multiple classes of classifiers. Reduced features, depending on the objective to be achieved, require fewer training rules, thereby improving the accuracy of the system. Learning is an important mechanism to automate fuzzy systems. The overall purpose of the research is to design a general fuzzy expert system with improvements in the relationship between interpretability and accuracy by improving the feature selection and learning mechanism processes through nature-inspired techniques or innovating new methodologies for the same

Background: External noise is a necessary factor for calculating the signal-to-noise ratio and evaluating communication quality and is one of the important parameters when designing low-frequency communication schemes or evaluating low-frequency signal quality. Methods: To study the radio noise distribution of each frequency point within the low-frequency band from 40kHz to 80kHz in a typical Southern China region at different time periods, the test team conducted a low-frequency radio noise test in Ziyang District, Yiyang City. Results and Discussion: The test data is analyzed and the results show that in this area, the radio noise in the 40kHz to 80kHz frequency band changes significantly day and night. The noise factors of most frequency points are in line with the predicted value given in the predicted value of the Radio Noise Volume of Recommendation ITU-R P.372-10. Conclusion: However, the noise distributions of 50kHz, 54kHz, 60kHz, and 68.5kHz are significantly higher than those of the predicted values.

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), lowdose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Results: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set.