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The aim of this study is to identify an alternative approach for simulating the in vitro fermentation and quantifying the production of rumen methane and rumen acetic acid during the rumen fermentation process with different total mixed rations. In this experiment, dietary nutrient compositions (neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), and dry matter (DM)) were selected as input parameters to establish three prediction models for rumen fermentation parameters (methane and acetic acid): an artificial neural network model, a genetic algorithm-bp model, and a support vector machine model. The research findings show that the three models had similar simulation results that aligned with the measured data trends (R 2 ≥ 0.83). Additionally, the root mean square errors (RMSEs) were ≤1.85 mL/g in the rumen methane model and ≤2.248 mmol/L in the rumen acetic acid model. Finally, this study also demonstrates the models' capacity for generalization through an independent verification experiment, as they effectively predicted outcomes even when significant trial factors were manipulated. These results suggest that machine learning-based in vitro rumen models can serve as a valuable tool for quantifying rumen fermentation parameters, guiding the optimization of dietary structures for dairy cows, rapidly screening methane-reducing feed options, and enhancing feeding efficiency.

In Global Navigation Satellite System (GNSS), a spoofing attack consists of forged signals which possibly cause the attacked receivers to deduce a false position, a false clock, or both. In contrast to simplistic spoofing, the induced spoofing captures the victim tracking loops by gradually adjusting it's parameters, e.g., code phase and power. Then the victims smoothly deviates from the correct position or timing. Therefore, it is more difficult to detect the induced spoofing than the simplistic one. In this paper, by utilizing the dynamic nature of such gradual adjustment process, an induced spoofing detection method is proposed based on the S-curve-bias (SCB). Firstly, SCB in the inducing process is theoretically derived. Then, in order to detect the induced spoofing, a detection metric is defined. After that, a series of experiments using the Texas spoofing test battery (TEXBAT) are performed to demonstrate the effectiveness of the proposed algorithm.

More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity.

DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.

Hiding capacity is very important for efficient covert communications. For JPEG2000 compressed images, it is necessary to enlarge the hiding capacity because the available redundancy is very limited. In addition, the bitstream truncation makes it difficult to hide information. In this paper, a high-capacity steganography scheme is proposed for the JPEG2000 baseline system, which uses bit-plane encoding procedure twice to solve the problem due to bitstream truncation. Moreover, embedding points and their intensity are determined in a well defined quantitative manner via redundancy evaluation to increase hiding capacity. The redundancy is measured by bit, which is different from conventional methods which adjust the embedding intensity by multiplying a visual masking factor. High volumetric data is embedded into bit-planes as low as possible to keep message integrality, but at the cost of an extra bit-plane encoding procedure and slightly changed compression ratio. The proposed method can be easily integrated into the JPEG2000 image coder, and the produced stego-bitstream can be decoded normally. Simulation shows that the proposed method is feasible, effective, and secure.

Currently, the nonadaptive delay-and-sum (DAS) beamformer is extensively used for ultrasound imaging, despite the fact that it has lower resolution and worse interference suppression capability than the adaptive standard Capon beamformer (SCB) if the steering vector corresponding to the signal of interest (SOI) is accurately known. The main problem which restricts the use of SCB, however, is that SCB lacks robustness against steering vector errors that are inevitable in practice. Whenever this happens, the performance of SCB may hecome worse than that of DAS. Therefore, a robust adaptive beamformer is desirable to maintain the robustness of DAS and adaptivity of SCB. In this paper we consider a recent promising robust Capon beamformer (RCB) for ultrasound imaging. We propose two ways of implementing RCB, one based on time delay and the other based on time reversal. RCB extends SCB by allowing the array steering vector to be within an uncertainty set. Hence, it restores the appeal of SCB including its high resolution and superb interference suppression capabilities, and also retains the attractiveness of DAS including its robustness against steering vector errors. The time-delay-based RCB can tolerate the misalignment of data samples and the time-reversal-based RCB can withstand the uncertainty of the Green's function. Both time-delay-based RCB and time-reversal-based RCB can be efficiently computed at a comparable cost to SCB. The excellent performances of the proposed robust adaptive beamforming approaches are demonstrated via a number of simulated and experimental examples.

To control high speed underwater vehicles, a proximity ranging system is needed to monitor the cavity thickness. In this paper, we study a time-of-flight (TOF) principle based acoustic proximity ranging system. By taking into account the acoustically hard boundary at the air-water interface, we first present a two-stage computationally efficient time delay estimation algorithm, referred to as the PEARS (Parameter Estimation for Acoustic Ranging Systems) algorithm, which is applicable to arbitrary transmitted waveforms. Numerical results based on a simulated waveform demonstrate that the PEARS estimates can approach the Cramér-Rao bound as the signal-to-noise ratio increases. We then present experiments performed by using commercially available acoustic transducers to further verify our method. To update TOF estimates quickly, a specially designed continuous wave (CW) is applied to the transducer. Experimental results show that PEARS can achieve high measurement accuracy for ranging distances less than 100 mm with an achievable parameter update rate of approximately 1.5 kHz.