Spectrum sensing in the low signal-to-noise ratio (SNR) environment is vital task for the evolution of cognitive radio technology. The numerous signal processing algorithms have since been proposed to improve the spec...
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Spectrum sensing in the low signal-to-noise ratio (SNR) environment is vital task for the evolution of cognitive radio technology. The numerous signal processing algorithms have since been proposed to improve the spectrum sensing performance. In the recent past, entropy based sensing methods are shown to be robust in a low SNR environment with small data sets. However, these methods only focus on information content and ignore temporal order of the signal. Hence, selection of appropriate entropy technique that considers both information content and temporal order is important. In addition, many works consider that the distribution of noise follows Gaussian under assumption that the sample size is infinity. The detection threshold designed using this assumption yield unreliable decisions. On the contrary, the captured data is limited in real-time and it should be minimum to reduce the computational complexity. To address these two issues, empirical permutation entropy with adaptive thresholding detection technique is proposed. Then, the work is extended to weighted gain cooperative sensing that uses Higuchi fractal dimension method to generate weight for each node. Simulation results reveal that the proposed method is robust, less sensitive to sample size, and improves the single node as well as multinode sensing performance.
A discrete resonant band-pass filter with a fast calculation algorithm, which can be used to perform discrete frequency transformations, is presented. The algorithm has low memory consumption requirements. It implemen...
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A discrete resonant band-pass filter with a fast calculation algorithm, which can be used to perform discrete frequency transformations, is presented. The algorithm has low memory consumption requirements. It implements a numerical integration method, simulating a harmonic resonator element modeled by the under-damped driven oscillator equations, expressed in a discrete form. The output from the presented filter is a discrete function with an amplitude of the steady-solution that closely matches the theoretical steady-solution amplitude of the continuous bandpass filter output. Multiple discrete resonant band-pass filters can be used to build a filter bank, which in turn can be used to perform a time-to-frequency transformation of discrete signals. The filter achieves a frequency and a time localization without utilizing the time windowing method. The presented stand-alone calculation algorithm related to this filter produces its output with a delay of just one sampling period. The algorithm's calculation cost is only 3 multiplications and 3 additions per sample, and does not require long memory buffers. The presented transformation does not surpass the precision of the Discrete Fourier and Discrete Wavelet Transformations. However, it may prove essential when the noise-artifacts of the near-real-world simulation are necessary in order to produce some specific auditory-perception phenomena. (C) 2016 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Ultrasonic inspection is nowadays widely used in material inspection to detect and locate internal defects. However, in case of large structures inspection, such as pipelines, vessel reactors etc., the amount of signa...
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ISBN:
(纸本)9783319238685;9783319238678
Ultrasonic inspection is nowadays widely used in material inspection to detect and locate internal defects. However, in case of large structures inspection, such as pipelines, vessel reactors etc., the amount of signals to acquire and interpret may be very important, especially in modern techniques that use image representations. In this article, a proposed algorithm to ultrasonic data selection and processing is described. Images will be replaced by 2 arrays that contain only the data regarding a detected defect. This allows avoiding the storage of useless received ultrasonic data, and simple pattern recognition techniques may be exploited to defect characterization.
The aim of this study is to discriminate the nature of cough by extracting its features time domain method. We are using two types of cough sound for prognosis purpose such as Productive and Nonproductive cough sounds...
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ISBN:
(纸本)9781479971657
The aim of this study is to discriminate the nature of cough by extracting its features time domain method. We are using two types of cough sound for prognosis purpose such as Productive and Nonproductive cough sounds. In this paper we are applying Digital signal processing algorithm for analysis of sounds. The feature extractions of sound detect the number of peaks and concentrate on phase two of sounds. So, by using this feature separation of productive and nonproductive cough sound is identified clearly which helps doctor for accurate diagnosis. Also, shown the impact of filters on signal of cough sounds. This study helps to health care professionals in their diagnosis.
When estimating the input-output characteristic of a static nonlinear function, input-referred noise may induce estimation bias, if model identification based on simple averages of input and output measurement data is...
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When estimating the input-output characteristic of a static nonlinear function, input-referred noise may induce estimation bias, if model identification based on simple averages of input and output measurement data is performed. This paper considers estimation of static nonlinearities based on polynomial functions and input-output averaged data. It first illustrates how the estimation bias originates and then it describes a procedure to compensate its effects. Both simulation and experimental results are shown. Experiments are carried out to estimate the voltage-to-voltage characteristic of a diode-based electrical circuit. Practical considerations are made regarding the minimum number of samples needed to perform compensation effectively.
In this paper we present several improvements in the Tongue Drive System magnetic sensor signal processing algorithm and its new 8-command classification performance by applying robust pattern recognition techniques t...
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ISBN:
(纸本)9781479923465
In this paper we present several improvements in the Tongue Drive System magnetic sensor signal processing algorithm and its new 8-command classification performance by applying robust pattern recognition techniques that are resistant to natural tongue movements and earth's magnetic field variations. Two additional commands have been added to the system for better compatibility with smartphone interfaces. For this algorithm support vector machine with radial basis function kernels has been proposed instead of the previous algorithm that was based on linear distance discrimination. The proposed algorithm has also been compared with two commonly used non-linear classifiers, the Neural Network and AdaBoost. Data was collected from six able-bodied subjects while they sat, walked and read a passage. The average false positive ratio while walking and talking was 2.73% error, and the overall system accuracy under normal conditions was 92.64% correctness, 2.87% misclassification error, and 4.49% rejection error.
Mobile signalprocessing applications have a limited energy budget and require resource-efficient processing elements. General purpose VLIW CPUs offer a high energy efficiency and allow for the execution of a wide ran...
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ISBN:
(纸本)9780769552491
Mobile signalprocessing applications have a limited energy budget and require resource-efficient processing elements. General purpose VLIW CPUs offer a high energy efficiency and allow for the execution of a wide range of applications in this domain. In this work we present the configurable 32 bit VLIW processor architecture CoreVA. Besides the number of issue slots, it allows for a fine-grained configuration of the amount and characteristics of the processor's functional units (e.g., ALUs, MACs, or LD/ST units). A design-space exploration is performed to evaluate how these functional units impact area and power consumption. The basic configuration with one ALU, MAC, DIV, and LD/ST unit has a power consumption of 11.796mW and an area of 0.142mm(2) at a clock frequency of 750 MHz in a 28 nm FD-SOI process. The maximum clock frequency in this process node is 833 MHz. To bear a relation of the hardware requirements to possible performance gains of the application, a signal processing algorithm is used as a benchmark to evaluate the energy consumption of different hardware configurations. The lowest energy consumption is observed with a configuration of 4 issue slots using 4 ALUs, 4 MACs, and 2 LD/ST units. This is an improvement by a factor of 1.68 compared to the single issue slot configuration.
A novel signal processing algorithm was proposed here for vibration signal analysis in condition monitoring and health diagnosis of rolling bearings. Such technique required an envelope being extracted from vibration ...
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ISBN:
(纸本)9781479936502
A novel signal processing algorithm was proposed here for vibration signal analysis in condition monitoring and health diagnosis of rolling bearings. Such technique required an envelope being extracted from vibration signal with Harmonic Wavelet Transform. The principal periodic component in the envelope was subsequently detected, enhanced and reconstructed with sweep frequency method based on singular value ratio (SVR) spectrum. Such signalprocessing approach was experimentally evaluated by using vibration signals measured on rolling element bearings that contained localized structural defects with proved validity and efficiency.
A new signal processing algorithm for absolute temperature measurement using white light interferometry has been proposed and investigated theoretically. The proposed algorithm determines the phase delay of an interfe...
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A new signal processing algorithm for absolute temperature measurement using white light interferometry has been proposed and investigated theoretically. The proposed algorithm determines the phase delay of an interferometer with very high precision (<< one fringe) by identifying the zero order fringe peak of cross-correlation of two fringe scans of white light interferometer. The algorithm features cross-correlation of interferometer fringe scans, hypothesis testing and fine tuning. The hypothesis test looks for a zero order fringe peak candidate about which the cross-correlation is symmetric minimizing the uncertainty of mis-identification. Fine tuning provides the proposed algorithm with high precision sub-sample resolution phase delay estimation capability. The shot noise limited performance of the proposed algorithm has been analyzed using computer simulations. Root-mean-square (RMS) phase error of the estimated zero order fringe peak has been calculated for the changes of three different parameters (SNR, fringe scan sample rate, coherence length of light source). Computer simulations showed that the proposed signal processing algorithm identified the zero order fringe peak with a miss rate of 3 x 10(-4) at 31 dB SNR and the extrapolated miss rate at 35 dB was 3 x 10(-8). Also, at 35 dB SNR, RMS phase error less than 10(-3) fringe was obtained. The proposed signal processing algorithm uses a software approach that is potentially inexpensive, simple and fast.
Safety applications require fast, precise and highly reliable sensors at low costs. This paper presents signalprocessing methods for an active multispectral optical point sensor instrumentation for which a first tech...
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Safety applications require fast, precise and highly reliable sensors at low costs. This paper presents signalprocessing methods for an active multispectral optical point sensor instrumentation for which a first technical implementation exists. Due to the very demanding requirements for safeguarding equipment, these processing methods are targeted to run on a small embedded system with a guaranteed reaction time T < 2ms and a sufficiently low failure rate according to applicable safety standards, e.g., ISO-13849. The proposed data processing concept includes a novel technique for distance-aided fusion of multispectral data in order to compensate for displacement-related alteration of the measured signal. The distance measuring is based on triangulation with precise results even for low-resolution detectors, thus strengthening the practical applicability. Furthermore, standard components, such as support vector machines (SVMs), are used for reliable material classification. All methods have been evaluated for variants of the underlying sensor principle. Therefore, the results of the evaluation are independent of any specific hardware.
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