Digital signal processing

Specific emitter identification (SEI)  is a promising authentication paradigm in physical layer security (PLS). Despite the significant success of existing SEI schemes, most of them assume that the distributions of the training dataset and the test dataset are consistent. However, in most practical scenarios, when the signal parameters change, the distribution of the samples will changes,  resulting in a significant performance degradation.

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The file ExplanationForPaper.m contains the code that draws the figures in "Periodograms and The Method of Averaged Periodograms."  It also produces many, many other, related figures and performs many related calculations.  By reading through the code and running it, the reader will be able to "experience" the mateiral presented in the article.  The reader will also see material related to the additional application discussed at the end of "The method of averaged periodograms" and just before the section "Numerical examples."

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The Intel D435i camera is selected to collect the point cloud data of lower limbs,the point cloud data source was 10 subjects including 4 females and 6 males, subjects are informed and voluntary, aged between 23-26 years, with an average age of 24.3 (±1.03) years, height of 172.1 (±6.46) cm, and weight of 71.3 (±9.58 kg.). The subjects were not trained prior to the testThe experimental data from these ten individuals were divided into two parts, half for training the long and short term memory neural network and half for validating the real-time and accuracy of the training model.

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This dataset focuses on cooperative spectrum sensing in a cognitive radio network, where multiple secondary users collaborate to detect the presence of a primary user. We introduce multiple cooperative spectrum sensing schemes based on a tree deep neural network architecture, incorporating a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity pattern of the primary user.

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851 Views

The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information.

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In this investigation, the researchers have used a commercially available millimeter-wave (MMW) radar to collect data and assess the performance of deep learning algorithms in distinguishing different objects. The research looks at how varied ambiance factors, such as height, distance, and lighting, affect object recognition ability in both static and dynamic stages of the radar.

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The growth of the use of the Linux operating system in embedded systems projects brings to the spotlight essential questions about the capabilities of this operating system in real-time systems, in particular, soft real-time systems. In this context, the quantitative analysis of Linux-based embedded systems is the focus of this paper, which includes the evaluation of the latency time, jitter, and worst-case response time.

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Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of  the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers.

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