Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture Detection

Noise reduction in medical image analysis is still an interesting hot topic, especially in the field of ultrasonic images. Actually, a big concern has been given to automatically reducing noise in human-bone ultrasonic computed tomography (USCT) images. In this chapter, a new hardware prototype, called USCT, is used but images given by this device are noisy and difficult to interpret. Our approach aims to reinforce the peak signal-to-noise ratio (PSNR) in these images to perform an automatic segmentation for bone structures and pathology detection. First, we propose to improve USCT image quality by implementing the discrete wavelet transform algorithm. Second, we focus on a hybrid algorithm combining the k-means with the Otsu method, hence improving the PSNR. Our assessment of the performance shows that the algorithmic approach is comparable with recent methods. It outperforms most of them with its ability to enhance the PSNR to detect edges and pathologies in the USCT images. Our proposed algorithm can be generalized to any medical image to carry out automatic image diagnosis due to noise reduction, and then we have to overcome classical medical image analysis by achieving a short-time process

Detection of Primary User assisted by Machine Learning over Multipath Channels

International audienceIn this paper, we provide new blind spectrum sensing (SS) methods based on a machine learning (ML) model to overcome the effects of multipath channels. We introduce three ML methods in order to improve the detection of the primary user (PU) in a cognitive radio network in severe multipath environment. The ML approaches proposed here are the Naive Bayes Classifier (NBC), the Support Vector Machine (SVM) and the Artificial neural network (ANN). The non-linear separation of the training samples provided by ML features is a good alternative to avoid miss-detection or false alarm obtained with classical fixed threshold detection. Simulations shows that the proposed algorithms outperform classical non-cooperative SS algorithms based on the eigenvalues of the covariance matrix of the received signal. The proposed SS detectors based on ML algorithms are concluded to be good candidates for PU detection over multipath channels as in indoor scenarios, especially in low signal to noise ratios (SNR)

Spectrum sensing assisted by windowing for fast time-varying channel

International audienceIn this paper, we introduce new totally blind spectrum sensing (SS) algorithms, for fast time-varying channel, based on eigenvalue decomposition (EVD) of the covariance matrix of the received signal. The new scheme is based on the sliding window whose the size depends on the coherence time of the channel. First, we evaluate the impact of the mobility on the detection performance. Then, by applying EVD in each window, we focus our study on the maximal estimated largest eigenvalue (MELE). We provide simulation results in order to validate the proposed theoretical expression of the probability density function of the MELE. Finally, simulation results illustrate the performance of the contributions and are compared to other SS methods

Blind Spectrum Sensing Using Extreme Eigenvalues for Cognitive Radio Networks

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On the impact of the covariance matrix size for spectrum sensing methods:beamforming versus eigenvalues

eISBN 978-1-7281-2999-0Session 3 - Wireless Data CommunicationInternational audienceIn this paper, we investigate the impact of the covariance matrix (CM) size of the received signal on the performance of spectrum sensing methods. Our analysis is based on two well-known methods of spectrum sensing (SS), one based on the eigenvalues called maximum-to minimum eigenvalue (MME) and the other is based on the beamforming technique called maximum-to-minimum beam energy (MMBE). From an analytical development, we provide an explanation about the gap of detection performance between the two algorithms according to the size of the CM. Finally, we provide some simulations results to show the impacts of the smoothing factor and the number of antennas on the detection performance for both methods. These two parameters define the size of the CM of the received signal

SVM Assisted Primary User-Detection for Non-Cooperative Cognitive Radio Networks

International audienceThis paper presents a new blind spectrum sensing (SS) algorithm based on a machine learning model: the radial basis function support-vector machines (RBF-SVM). As features, the introduced approach uses statistical tests that are based on the eigenvalues of the received signals covariance matrix. Since the decision on the frequency resource occupancy is in fact an issue of labeling binary data, SVM is intended as a potential technique for SS paradigm. The flexibility of SVM for linearly non-separable and high dimensional data makes it a good candidate for our issue, particularly that we consider low signal to noise ratios (SNR). Computer simulations shows that the proposal outperforms classical non-cooperative SS algorithms. © 2020 IEEE

Smart Full-Exploitation of Beamforming Fusion assisted Spectrum Sensing for Cognitive Radio

International audienceThis paper proposes blind spectrum sensing (SS) in a narrowband context called Beamforming Fusion assisted Spectrum Sensing (BFSS). Considering a channel with angles of arrival (AoA), we jointly exploit beamforming algorithms to make decisions about the detection of users on frequency resources. The proposed method is totally blind and does not require knowledge of the noise power, the channel estimation, and the source signal. A state-of-the-art comparison of SS methods using beamforming is provided to validate our contribution in a shallow SNR region

Chaotic Dingo Optimization Algorithm: Application in Feature Selection for Beamforming Aided Spectrum Sensing

International audienceSpectrum sensing based on Beamforming, like others classification problem, require feature selection to perform learning algorithms and enhance the classification task. This paper proposes a novel version of the Dingo Optimization Algorithm (DOA) to optimize feature selection for a Deep Neural Network (DNN) classifier. Two improvements are introduced to avoid the premature convergence problem and stagnation in the local optima of the original DOA. First, the chaos strategy is executed to produce a high level of diversification in the algorithm, which improves its ability to escape from potential local optimums. Second, the weight factor is introduced to boot up the search process to the global optima. Here, the aim is to improve the DOA for feature selection in the deep learning approach in order to enhance the performance of blind spectrum sensing based on Beamforming in the context of cognitive radio (CR). Through simulations results, we illustrate that our algorithm, called Chaotic Dingo Optimization Algorithm (CDOA), outperforms the original one and a set of state-of-the-art optimization algorithms (i.e., HS, BBO, PSO, and SA) for feature selection in the learning approach