Introduction to Signal Processing

Hippenstiel, Ralph D.

doi:10.1201/9781420042047-3

Cited by 4 publications

(7 citation statements)

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“…Then, SNR for X with respect to Y is calculated by summing the squared magnitudes in the two different bands as in Eq. ( 1) [15]. The chosen bands are a low band that ranges from 50 Hz to 3 kHz and a high band that ranges from 6 kHz to 8.5 kHz.…”

Section: In Band Signal To Noise Ratio (Snr)mentioning

confidence: 99%

Low-cost noise maker for testing acoustically guided unmanned vehicles

Mousa,

Tork,

Abdallah

2023

J. Phys.: Conf. Ser.

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Testing the performance of the Acoustically Guided Unmanned Underwater Vehicle (AGUUV) prior to its deployment is essential to ensure its ability to perform the required tasks accurately. These systems can be deployed from boats or helicopters to scan areas of military and economic importance for threats. These threats such as aircrafts carriers, ships, submarines, divers, and autonomous surface vehicles. The problem of implementing a low-cost high-performance noise maker (LCNM) which is suitable for being used as a test target instead of different threats for AGUUVs is the motivation of this paper. Lab experiments and sea trials are performed using the proposed noise maker to demonstrate the proper operation of the AGUUV. Lab experiments showed that the radiated noise of the proposed noise maker has high power in the frequency band between 5 to 8 kHz. The measured signal to noise ratio (SNR) for the introduced LCNM is 30 dB in this band. The experimental results presented in this paper show the feasibility of using the proposed LCNM effectively to test the AGUUVs.

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Section: In Band Signal To Noise Ratio (Snr)mentioning

confidence: 99%

Low-cost noise maker for testing acoustically guided unmanned vehicles

Mousa,

Tork,

Abdallah

2023

J. Phys.: Conf. Ser.

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“…Basic statistical calculations are, of course, very easy on computer applications [4]. Many signal processing applications are used in everyday life such as digital cameras, radar signal detection [5], video processing, processing of various sensor arrays, so the method of processing and transmitting data must be efficient [6].…”

Section: Introductionmentioning

confidence: 99%

Implementation of first order statistical processor on FPGA for feature extraction

Hadiyoso,

Ramdani,

Irawati

et al. 2024

IJRES

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Statistical calculations on signals commonly used in feature extraction. In software processing, statistical computation is an easy task. However, providing a computer requires high costs for simple statistical processing. Another consideration is the need for implementation with real-time and portable processing. Therefore, an alternative device is needed, one of which is the field programmable gate array (FPGA). FPGA is a logic circuit board that can be reconfigured according to computing needs. FPGA can also be used as a prototyping of electronic chips. However, implementing statistical formulas in FPGA is interesting in developing its architecture. Therefore, this research proposes a logic circuit design that can be used for first-order statistical calculations. Statistical parameters include the mean, variance, standard deviation, skewness, and kurtosis. The validation test was performed on the electrocardiogram (ECG) signal series and compared with manual calculations. Validation shows that the mean and variance has very high accuracy with an average error of less than 0.06%.

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“…The nonblind spectrum sensing schemes require both accurate statistical models of the PU's signal and noise. For example, derived from the log-likelihood ratio (LLR) detection, the estimatorcorrelator (E-C) detection has been proved to achieve the theoretical optimum [2] and provide perfect knowledge of both PU signals and noises. Accordingly, Gardner designed cyclostationary detection by exploiting the cyclostationarity attributes of received signals, which can differentiate noise from primary users' signals [3].…”

Section: Introductionmentioning

confidence: 99%

Multiband Cooperative Spectrum Sensing Meets Vehicular Network: Relying on CNN-LSTM Approach

Wang³

et al. 2023

Wireless Communications and Mobile Computing

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A vehicular network is expected to empower all aspects of the intelligent transportation system (ITS) and aim at improving road safety and traffic efficiency. In view of the fact that spectrum scarcity becomes more severe owing to the increasing number of connected vehicles, implying spectrum sensing technology in vehicular network, i.e., cognitive vehicular network, has emerged as a promising solution to provide opportunistic usage of licensed spectrum. However, some unique features of vehicular networks, such as high movement and dynamic topology, take on high challenges for spectrum sensing. Recently, machine learning-based approaches, especially deep learning, for spectrum sensing have attracted sufficient interest. In this work, we investigate a learning-based cooperative spectrum sensing (CSS) approach for multiband spectrum sensing in the cognitive vehicular network. Specifically, we integrate two powerful deep learning models, i.e., the convolutional neural network (CNN) to exploit the features from sensing data, and the long-short-term memory (LSTM) network is then utilized to extract temporal correlations given input as the generated features by the CNN structure. Instead of the predefined decision threshold typically set in conventional approaches, our proposed approach could eliminate the impact of impertinent threshold value setting. Extensive simulations have been conducted to evaluate the effectiveness of the proposed method in achieving satisfactory spectrum sensing performance, particularly in terms of higher detection accuracy, robustness in low signal-to-noise ratio (SNR) environments, and a significant reduction in spectrum sensing time compared to other methods.

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Introduction to Signal Processing

Cited by 4 publications

References 0 publications

Low-cost noise maker for testing acoustically guided unmanned vehicles

Low-cost noise maker for testing acoustically guided unmanned vehicles

Implementation of first order statistical processor on FPGA for feature extraction

Multiband Cooperative Spectrum Sensing Meets Vehicular Network: Relying on CNN-LSTM Approach

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