Frequency-domain Detection Method Research Articles

Research on Frequency Domain Detection Method for 1553B Bus Based on Dual-Coaxial Mechanism

Research on Frequency Domain Detection Method for 1553B Bus Based on Dual-Coaxial Mechanism

Noise Optimization Design of Frequency-Domain Air-Core Sensor Based on Capacitor Tuning Technology.

In the semi-aviation frequency-domain electromagnetic measurement, the induction air-core coil and the differential pre-amplifier circuit introduce noise, which affects the sensor and results in receiving weak signals and improving the signal-to-noise ratio of the system. In response to this problem, by analyzing the physical structure of the air-core coil sensor and the mechanism of the amplification circuit, combined with the simulation and experimental tests of voltage noise, current noise, resistance noise and other noise components, analyzed that the thermal noise is the main component of the sensor noise in the system frequency band, but directly removing the matching resistor increases the instability of the circuit, causes the coil to work in an underdamped state, and generates a time domain oscillation at the resonant frequency, source impedance analysis and analysis of differential pre-amplifier circuit in the frequency-domain detection method, abandoning the matching resistance scheme and magnetic flux negative feedback scheme. The matching capacitor is added to make the receiver detect the frequency range in the 1–10 kHz range. In normal operation, the noise level reaches 10 nV level, which not only increases the stability of the circuit, but also reduces the noise of the sensor. It has far-reaching significance for the detection of weak frequency signals.

LPI radar signal detection based on the combination of FFT and segmented autocorrelation plus PAHT

This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.

Voiced/nonvoiced detection in compressively sensed speech signals

We leverage the recent algorithmic advances in compressive sensing (CS), and propose a novel unsupervised voiced/nonvoiced (V/NV) detection method for compressively sensed speech signals. It attempts to exploit the fact that there is significant glottal activity during production of voiced speech while the same is not true for nonvoiced speech. This characteristic of the speech production mechanism is captured in the sparse feature vector derived using CS framework. Further, we propose an information theoretic metric, for V/NV classification, exploiting the sparsity of the extracted feature using a signal adaptive dictionary motivated by speech production mechanism. The final classification is done using an adaptive threshold selection scheme, which uses the temporal information of speech signals. While existing methods of feature extraction use speech samples directly, proposed method performs V/NV detection in compressively sensed speech signals (requiring very less memory), where existing time or frequency domain detection methods are not directly applicable. Hence, this method can be effective for various speech applications. Performance of the proposed method is studied on CMU-ARCTIC database, for eight types of additive noises, taken from the NOISEX database, at different signal-to-noise ratios (SNRs). The proposed method performs similar or better compared to the existing methods, especially at lower SNRs and this provide compelling evidence of the effectiveness of sparse feature vector for V/NV detection.

Analytical BER and PER Performance of Frequency-Domain Diversity Combining, Multipacket Detection and Hybrid Schemes

Diversity Combining (DC) and Multipacket Detection (MPD) are efficient techniques that cope with packet errors due to severe propagation conditions and/or collisions. To evaluate their network performance on a network simulator, an accurate characterization of the Bit Error Rate (BER) and Packet Error Rate (PER) can successfully replace the behavior of the PHY layer. This choice is better in terms of computational time than implementing a full PHY layer to be jointly simulated with the upper layers. In this paper, an analytical model is proposed to compute the BER and PER of DC, MPD and hybrid techniques, considering a Single-Carrier with Frequency-Domain Equalization (SC-FDE) scenario. The paper also considers two frequency-domain detection methods: a simple linear receiver and a powerful iterative receiver. The proposed model is simple and versatile, since it can support nonhomogeneous transmitting powers, and different channel conditions concerning retransmissions and channel randomization techniques. Several simulations show that the proposed model provides accurate BER and PER results for a wide range of scenarios.

A novel approach to laser tomography

By combining near-infrared intensity-modulated illumination with frequency domain detection methods, we have obtained the first detailed optical images of thick tissues (1–3 cm). The raw data of these images are characterized by millimetre resolution and high contrast. The method employs a pulsed laser coupled to a cross-correlation frequency domain detection scheme. The near-infrared imaging method is non-invasive and inexpensive, uses non-ionizing radiation and is potentially fast enough for real-time imaging in the seconds time range. In the frequency domain, diffusional wave optics provides the framework for a description of the light propagation in tissues. Our approach results in a theoretically and experimentally simple way to exploit the advantages of time resolution in optical imaging of inhomogeneous highly scattering materials. At any given modulation frequency, the light wavefront advances at constant velocity in a spherical wave. Objects with scattering or absorption coefficients that are different from the surrounding medium cause a deformation of the propagating wavefront that can be accurately measured using frequency domain detection methods. The visualization (on a computer screen) of these wavefront deformations provides a projection of the interior's image.

A statistical method to determine signal processing and stimulus parameter settings for optimal evoked potential detection

A frequency-domain detection method, based on statistical estimates derived from coherence measures, was used to determine optimal signal processing and stimulus parameter settings. The method was evaluated using simulations of electrophysiologic recording noise and signals in noise, as well as, real physiologic noise and click evoked auditory brain-stem responses. Parameters varied in the study were artifact rejection level, spectral filter shape, and stimulus presentation rate. Signal detection was improved following objective selection of parameter settings by this method. Some systematic estimate biases were found under the noise conditions evaluated. This method should be useful in selecting stimulus and recording parameter settings for improved signal detectability. [Work supported by U.C. San Francisco Graduate Division.]