Search Results (4)

To address the underutilization of Schizochytrium limacinum meal, polysaccharides from Schizochytrium limacinum meal (SLMPs) were prepared via ultrasonic-assisted eutectic-solvent-based extraction. Although polysaccharides exhibit promising application potential, the structural ambiguity of SLMPs necessitates systematic investigation to elucidate their structure–activity relationships, thereby providing a scientific foundation for their subsequent development and utilization. Using response-surface methodology (RSM), the optimized extraction conditions were determined as follows: ultrasonic temperature of 52 °C, ultrasonic duration of 31 min, ultrasonic power of 57 W, water content of 29%, and a material-to-liquid ratio of 1:36 g/mL. Under these conditions, the maximum polysaccharide yield reached 9.25%, demonstrating a significant advantage over the conventional water extraction method (4.18% yield). Following purification, the antioxidant activity and structural characteristics of SLMPs were analyzed. The molecular weight, monosaccharide composition, reducing groups, and higher-order conformation were systematically correlated with antioxidant activity. Fourier-transform infrared spectroscopy (FTIR), monosaccharide composition analysis, and ¹H nuclear magnetic resonance (NMR) spectroscopy revealed characteristic polysaccharide functional groups (C–O, O–H, and C=O). Monosaccharides such as glucose (Glc), galactose (Gal), and arabinose (Ara) were found to enhance antioxidant activity. High-performance gel permeation chromatography (HPGPC) indicated a molecular weight of 20.7 kDa for SLMPs, with low-molecular-weight fractions exhibiting superior antioxidant activity. Scanning electron microscopy (SEM) further demonstrated that ultrasonically extracted polysaccharides possess porous structures capable of chelating redox-active functional groups. These findings suggest that ultrasonic-assisted eutectic-solvent-based extraction is an efficient method for polysaccharide extraction while preserving antioxidant properties. Full article

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB. Full article

The physical parameters (stiffness, damping) of time-varying (TV) systems under random excitation provide valuable information for their working condition but they are often overwhelmed by noise interference. To overcome this problem, this paper presents a novel multi-level kernel regression residual decomposition method, which can not only effectively separate each modal component from the raw vibration acceleration signal, but also eliminate noise interference. Additionally, the multiple degree-of-freedom (DOF) parameter identification problem is transformed into a single DOF parameter identification problem. Combined with the derived polynomial frequency modulation integral algorithm and the cross-correlation theory based on the fractional Fourier ambiguity function, a physical parameter identification method is proposed. The method provides a new idea in modeling TV systems and identifying physical parameters under random excitation. To demonstrate the effectiveness of the proposed method, numerical simulations are conducted with three different cases of variation (variation, quadratic variation, and periodic variation) in time. Moreover, its robustness is evaluated by adding different signal-to-noise ratio levels of noise (20 dB, 50 dB, 100 dB) to the input vibration acceleration signal. The analysis results confirm the performance of the proposed method for the parameter identification of TV systems under random excitation. Full article

Speech emotion recognition is an important part of human–computer interaction, and the use of computers to analyze emotions and extract speech emotion features that can achieve high recognition rates is an important step. We applied the Fractional Fourier Transform (FrFT), and then constructed it to extract MFCC and combined it with a deep learning method for speech emotion recognition. Since the performance of FrFT depends on the transform order $p$, we utilized an ambiguity function to determine the optimal order for each frame of speech. The MFCC was extracted under the optimal order of FrFT for each frame of speech. Finally, combining the deep learning network LSTM for speech emotion recognition. Our experiment was conducted on the RAVDESS, and detailed confusion matrices and accuracy were given for analysis. The MFCC extracted using FrFT was shown to have better performance than ordinal FT, and the proposed model achieved a weighting accuracy of 79.86%. Full article